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• W4376875175 endingPage "1748" @default.
• W4376875175 startingPage "1748" @default.
• W4376875175 abstract "Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations." @default.
• W4376875175 created "2023-05-18" @default.
• W4376875175 creator A5016153721 @default.
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• W4376875175 creator A5063091955 @default.
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• W4376875175 creator A5075056699 @default.
• W4376875175 date "2023-05-16" @default.
• W4376875175 modified "2023-09-30" @default.
• W4376875175 title "Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview" @default.
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